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Tytuł pozycji:

Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes.

Tytuł:
Integrating human behavior and snake ecology with agent-based models to predict snakebite in high risk landscapes.
Autorzy:
Eyal Goldstein
Joseph J Erinjery
Gerardo Martin
Anuradhani Kasturiratne
Dileepa Senajith Ediriweera
Hithanadura Janaka de Silva
Peter Diggle
David Griffith Lalloo
Kris A Murray
Takuya Iwamura
Temat:
Arctic medicine. Tropical medicine
RC955-962
Public aspects of medicine
RA1-1270
Źródło:
PLoS Neglected Tropical Diseases, Vol 15, Iss 1, p e0009047 (2021)
Wydawca:
Public Library of Science (PLoS), 2021.
Rok publikacji:
2021
Kolekcja:
LCC:Arctic medicine. Tropical medicine
LCC:Public aspects of medicine
Typ dokumentu:
article
Opis pliku:
electronic resource
Język:
English
ISSN:
1935-2727
1935-2735
Relacje:
https://doaj.org/toc/1935-2727; https://doaj.org/toc/1935-2735
DOI:
10.1371/journal.pntd.0009047
Dostęp URL:
https://doaj.org/article/f26ef41a2502430483e37931b7cbee15  Link otwiera się w nowym oknie
Numer akcesji:
edsdoj.f26ef41a2502430483e37931b7cbee15
Czasopismo naukowe
Snakebite causes more than 1.8 million envenoming cases annually and is a major cause of death in the tropics especially for poor farmers. While both social and ecological factors influence the chance encounter between snakes and people, the spatio-temporal processes underlying snakebites remain poorly explored. Previous research has focused on statistical correlates between snakebites and ecological, sociological, or environmental factors, but the human and snake behavioral patterns that drive the spatio-temporal process have not yet been integrated into a single model. Here we use a bottom-up simulation approach using agent-based modelling (ABM) parameterized with datasets from Sri Lanka, a snakebite hotspot, to characterise the mechanisms of snakebite and identify risk factors. Spatio-temporal dynamics of snakebite risks are examined through the model incorporating six snake species and three farmer types (rice, tea, and rubber). We find that snakebites are mainly climatically driven, but the risks also depend on farmer types due to working schedules as well as species present in landscapes. Snake species are differentiated by both distribution and by habitat preference, and farmers are differentiated by working patterns that are climatically driven, and the combination of these factors leads to unique encounter rates for different landcover types as well as locations. Validation using epidemiological studies demonstrated that our model can explain observed patterns, including temporal patterns of snakebite incidence, and relative contribution of bites by each snake species. Our predictions can be used to generate hypotheses and inform future studies and decision makers. Additionally, our model is transferable to other locations with high snakebite burden as well.
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